Prostate cancer detection using residual networks

Xu, Helen S.; Baxter, Jamie; Akın, Oğuz; Cantor-Rivera, Diego

doi:10.1007/s11548-019-01967-5

Cited by 31 publications

(18 citation statements)

References 10 publications

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“…The most general employed CNNs consist of a ~10-layer encoder network, after which the latent space is used for patientor lesion-wise classification [178][179][180]182,184,187,193] or subsequently expanded in a decoder configuration for full-image probability maps [173,174,176,189] . In some papers, residual connections within the encoding structure (e.g., ResNet architectures) are used to ease training and reduce the impact of vanishing gradients by preserving information for subsequent layers [175,190,193] . Auto-encoder networks enforcing sparsity or nonnegativity in the feature space have been developed for feature extraction [181,182,218] , subsequently employing other classification algorithms for a final classification output.…”

Section: Network Architecturesmentioning

confidence: 99%

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Wildeboer

Sloun

Wijkstra

et al. 2020

Computer Methods and Programs in Biomedicine

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DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

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Section: Network Architecturesmentioning

confidence: 99%

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Wildeboer

Sloun

Wijkstra

et al. 2020

Computer Methods and Programs in Biomedicine

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“…www.mdpi.com/journal/cancers DL techniques have also been applied to prostate lesion detection ( Table 2). Xu et al [84] implemented a type of neural network with extensive layers, ResNet [86], to find lesions on T2weighted, ADC, and DWI images. This study used images from the Cancer Imaging Archive data portal and included 346 patients.…”

Section: Prostate Lesion: Detection Segmentation and Volume Estimationmentioning

confidence: 99%

“…This study used images from the Cancer Imaging Archive data portal and included 346 patients. They achieved an AUC of 0.97 [84]. Tsehay et al [85] also used a DL algorithm with a 5-layer CNN architecture that used an individual loss function for each layer.…”

Section: Prostate Lesion: Detection Segmentation and Volume Estimationmentioning

confidence: 99%

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Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends

Bardis

Houshyar

Chang

et al. 2020

Cancers

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Prostate carcinoma is one of the most prevalent cancers worldwide. Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification. Machine learning (ML), a branch of artificial intelligence, can rapidly and accurately analyze mpMRI images. ML could provide better standardization and consistency in identifying prostate lesions and enhance prostate carcinoma management. This review summarizes ML applications to prostate mpMRI and focuses on prostate organ segmentation, lesion detection and segmentation, and lesion characterization. A literature search was conducted to find studies that have applied ML methods to prostate mpMRI. To date, prostate organ segmentation and volume approximation have been well executed using various ML techniques. Prostate lesion detection and segmentation are much more challenging tasks for ML and were attempted in several studies. They largely remain unsolved problems due to data scarcity and the limitations of current ML algorithms. By contrast, prostate lesion characterization has been successfully completed in several studies because of better data availability. Overall, ML is well situated to become a tool that enhances radiologists’ accuracy and speed.

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“…Their system showed that the performance of a U-Net classifier is similar to the clinical assessment. Xu et al [ 26 ] developed a system to detect prostate lesions using a residual network. Their system resulted in an accuracy of

.…”

Section: Introductionmentioning

confidence: 99%

Precise Identification of Prostate Cancer from DWI Using Transfer Learning

Abdelmaksoud

Shalaby

Mahmoud

et al. 2021

Sensors

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Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: The proposed system first uses non-negative matrix factorization (NMF) to integrate three different types of features for the accurate segmentation of prostate regions. Then, discriminatory features in the form of apparent diffusion coefficient (ADC) volumes are estimated from the segmented regions. The ADC maps that constitute these volumes are labeled by a radiologist to identify the ADC maps with malignant or benign tumors. Finally, transfer learning is used to fine-tune two different previously-trained convolutional neural network (CNN) models (AlexNet and VGGNet) for detecting and identifying prostate cancer. Results: Multiple experiments were conducted to evaluate the accuracy of different CNN models using DWI datasets acquired at nine distinct b-values that included both high and low b-values. The average accuracy of AlexNet at the nine b-values was 89.2±1.5% with average sensitivity and specificity of 87.5±2.3% and 90.9±1.9%. These results improved with the use of the deeper CNN model (VGGNet). The average accuracy of VGGNet was 91.2±1.3% with sensitivity and specificity of 91.7±1.7% and 90.1±2.8%. Conclusions: The results of the conducted experiments emphasize the feasibility and accuracy of the developed system and the improvement of this accuracy using the deeper CNN.

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Prostate cancer detection using residual networks

Cited by 31 publications

References 10 publications

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends

Precise Identification of Prostate Cancer from DWI Using Transfer Learning

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